Smart Wearable Health Pendant Using IoT and Machine Learning for Heart Rate Monitoring and Detecting Falls

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 16 | 02 | Page :
    By

    Aditya R. Shinde,

  • Vaibhavi C. Mahadik,

  • Ranveer R. Patil,

  • Varad V. Patwari,

  • P. S. Kedge,

  • S. B. Patil,

  1. Student, Department of Computer Engineering, Savitribai Phule Pune University, Pune, Maharashtra, India
  2. Student, Department of Computer Engineering, Savitribai Phule Pune University, Pune, Maharashtra, India
  3. Student, Department of Computer Engineering, Savitribai Phule Pune University, Pune, Maharashtra, India
  4. Student, Department of Computer Engineering, Savitribai Phule Pune University, Pune, Maharashtra, India
  5. Assistant Professor, Department of Computer Engineering, Savitribai Phule Pune University, Pune, Maharashtra, India
  6. Professor, Department of Electronics Telecommunication Engineering, Savitribai Phule Pune University, Pune, Maharashtra, India

Abstract

Cardiovascular illnesses and accidental falls frequently occur among individuals and at-risk populations. This is the reason we genuinely require a device that can monitor their well-being continuously. We are referring to a health pendant designed for seniors and groups at risk can wear. This health pendant leverages Internet of Things technology and machine learning to monitor heart rate and identify falls. Our health pendant system incorporates an ESP32 microcontroller, a heart rate sensor, and an MPU6050 accelerometer and gyro sensor. These elements assist us in gathering data regarding individuals’ health and activities. We employ machine learning techniques.to examine the data from the sensor. This allows us to determine whether the heart rate is abnormal or if the individual has collapsed. We utilize the Pushbullet API to notify individuals who are looking after them We also have a button to reset the alarm if it is not an emergency. Our experiments show that our smart health pendant works well. It can send alerts on time to prevent problems and keep people safe. The good thing about our health pendant system is that it is portable, reliable and smart. This makes it a great tool for monitoring health. We think our smart health pendant is an idea because it can help elderly people and vulnerable groups. They can wear a health pendant. Get help when they need it. Our smart health pendant is good for people who need to be taken care of. It can send alerts to caregivers. This helps them take care of these people. We like our health pendant because it uses Internet of Things technology and machine learning. This makes it a great tool for health monitoring.

Keywords: Smart health pendant, Wearable health monitoring, Internet of Things (IoT), Machine learning, Fall detection, Heart rate monitoring, ESP32 microcontroller, MPU6050 sensor, Real-time alerts, Pushbullet API, Firebase, SOS emergency system, Elderly care, Health monitoring system, Portable medical device

How to cite this article:
Aditya R. Shinde, Vaibhavi C. Mahadik, Ranveer R. Patil, Varad V. Patwari, P. S. Kedge, S. B. Patil. Smart Wearable Health Pendant Using IoT and Machine Learning for Heart Rate Monitoring and Detecting Falls. Journal of Instrumentation Technology & Innovations. 2026; 16(02):-.
How to cite this URL:
Aditya R. Shinde, Vaibhavi C. Mahadik, Ranveer R. Patil, Varad V. Patwari, P. S. Kedge, S. B. Patil. Smart Wearable Health Pendant Using IoT and Machine Learning for Heart Rate Monitoring and Detecting Falls. Journal of Instrumentation Technology & Innovations. 2026; 16(02):-. Available from: https://journals.stmjournals.com/joiti/article=2026/view=245584


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Ahead of Print Subscription Review Article
Volume 16
02
Received 17/04/2026
Accepted 22/04/2026
Published 01/06/2026
Publication Time 45 Days
12

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